Autonomous driving has been an important topic of research in recent years.
In this thesis we study its application to Heavy Duty Vehicles, more specically,
vehicles consisting of a truck and a trailer. An overview study is done on
three fundamental steps of an autonomous driving system, planning, trajectory
tracking and obstacle avoidance.
In the planning part, we use RRT, and two other variants of the algorithm
to nd trajectories in an unstructured environment,
e.g., a mining site. A
novel path optimization post-processing technique well suited for use with RRT
solutions was also developed.
For the trajectory tracking task several well-known controllers were tested,
and their performance compared. An extension is proposed to one of the controllers
in order to take into account the trailer. The performance evaluation
was done on scaled truck systems in the Smart Mobility Lab at KTH.
The obstacle avoidance is done with the aid of a simple, yet functional Model
Predictive Controller. For this purpose, we developed dierent formulations
of the optimization problem, corresponding to distinct optimization goals and
vehicle models, in order to assess both the quality of the MPC, and of the
assumed truck model.
The outcome of this thesis is a fully autonomous system, able to plan and
move in constrained environments, while avoiding unpredicted obstacles. It
was implemented using a 1:32 scale remote controlled truck, commanded by a
2014. , 110 p.